Are Labels Necessary for Neural Architecture Search?cxliu/slides/unnas-talk-eccv-short.pdfFull con...

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Are Labels Necessary for Neural Architecture Search? Chenxi Liu, Piotr Dollár, Kaiming He, Ross Girshick, Alan Yuille, Saining Xie Spotlight @ECCV 2020 (Short Summary)

Transcript of Are Labels Necessary for Neural Architecture Search?cxliu/slides/unnas-talk-eccv-short.pdfFull con...

Page 1: Are Labels Necessary for Neural Architecture Search?cxliu/slides/unnas-talk-eccv-short.pdfFull con Subsampling Gaussian connections ection Convolutions Full connection 224 224 Strid

Are Labels Necessary for Neural Architecture Search?

Chenxi Liu, Piotr Dollár, Kaiming He, Ross Girshick, Alan Yuille, Saining Xie Spotlight @ECCV 2020 (Short Summary)

Page 2: Are Labels Necessary for Neural Architecture Search?cxliu/slides/unnas-talk-eccv-short.pdfFull con Subsampling Gaussian connections ection Convolutions Full connection 224 224 Strid

Current Universe

Parallel Universe

( , 6) ( , ship) ( , panda)

Designing neural architectures

( , 6) ( , ship) ( , panda)❌ ❌ ❌

What neural architectures will we find? Will they look similar to those on the left? Will they work as well as those on the left?

Page 3: Are Labels Necessary for Neural Architecture Search?cxliu/slides/unnas-talk-eccv-short.pdfFull con Subsampling Gaussian connections ection Convolutions Full connection 224 224 Strid

Architecture rankings produced with and without labels are highly correlated, across: • 3 unsupervised tasks • 2 datasets • 2 search spaces

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Sample-based experiments

Status quoOurs

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Search-based experiments

Architectures searched without labels are highly competitive, across: • 3 unsupervised search tasks • 3 search datasets • 2 supervised evaluation tasks • 2 evaluation datasets

Status quo Ours

Page 5: Are Labels Necessary for Neural Architecture Search?cxliu/slides/unnas-talk-eccv-short.pdfFull con Subsampling Gaussian connections ection Convolutions Full connection 224 224 Strid

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Search-based experiments

Architectures searched without labels are highly competitive, across: • 3 unsupervised search tasks • 3 search datasets • 2 supervised evaluation tasks • 2 evaluation datasets

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To perform NAS successfully, labels are not necessary

Thank you!